Building a Voice-Controlled Local AI Agent Using Whisper and Ollama
This article explores building a local AI agent that can understand spoken commands, interpret user intent, and execute actions like file creation, code generation, and text summarization through a web interface.
Why it matters
This project demonstrates how to build a voice-controlled AI agent that can perform useful tasks, highlighting the potential of integrating speech interfaces and language models in practical applications.
Key Points
- 1Modular pipeline for audio input, speech-to-text, intent detection, and tool execution
- 2Uses Whisper for speech-to-text and a hybrid approach for intent detection (rule-based and LLM-based)
- 3Generates code and performs file operations in a restricted directory to ensure safety
- 4Streamlit-based user interface provides transparency into each stage of the pipeline
Details
The system follows a modular pipeline: Audio Input → Speech-to-Text → Intent Detection → Tool Execution → UI Output. The speech-to-text is handled by the Whisper model, with performance optimizations like using a smaller model and caching. Intent detection uses a hybrid approach, with rule-based classification for common patterns and a local LLM (Ollama) for ambiguous inputs. Filenames are extracted directly from the transcribed text using regex. The system can create new files, write code generated by the LLM, and summarize text. Challenges faced include model latency, incorrect intent classification, filename extraction issues, and file overwrite logic, which were addressed through various solutions.
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